Natural Language Processing (NLP), in which computers are taught to understand written and spoken human language, is foundational to the increasing levels of automation being brought about by Artificial Intelligence (AI). Its application in translating languages, converting text to voice and voice to text highlights numerous possibilities to alleviate repetition and save time in sifting through mountains of data in documents or driving new levels of customer service.
Augmenting the work people do and bringing automation to such tasks as document searches, archive reviews and evidence analysis, to name a few, is an extremely valuable capability in numerous areas.
NLP in Banking
Banking is a document and text-heavy sector, with reams of data generated in day-to-day activities, such as mortgage and credit applications, regulatory financial statements, and customer service interactions. NLP can help convert large volumes of text and speech data into actionable insights, saving time and creating competitive advantage.
Banks are using NLP to automate certain document processing, analysis, and customer service activities. Some examples include intelligent document searches, investment and sentiment analysis and customer service chatbots, all used to streamline operations and allow timely investment or loan decisions. Along with these operational advantages, banks could use NLP to extract certain types of customer data that they don’t have time to track, such as propensity and ability of customers to buy a home. This data could help predict future customer needs and identify cross-selling opportunities.
NLP in Healthcare
Healthcare, another document and text-heavy sector, is experiencing tremendous growth in data, not just from general daily operations, but also from Electronic Health Records (EHR), sensors, wearables, and imaging systems such as X-Rays and MRIs. And the need to manage and make sense of it all — this is where AI and NLP can make a tangible impact.
In general, the adoption of NLP in Healthcare can lead to better patient outcomes, better drugs, and improved medical products. In addition, NLP can help enhance the accuracy and completeness of EHR by transforming free text into standardized data, or vice versa, such as dictation and transcription of physician notes into documented data. This capability alone allows doctors to spend more time with patients. Another great example is an NLP-enhanced EHR portal that empowers patients, helping them get a better understanding of symptoms using intelligent chatbots. These same NLP-enhanced EHR portals could be used to track and pinpoint patients requiring improved care, and/or detect patients with complex health conditions who may have a history of mental health or substance abuse.
NLP in Public Sector
Government and the Public Sector are often equated with scads of paperwork and bureaucratic inefficiencies. However, NLP may help to transform this image by reducing manual text generation and exploiting the large amounts of text data. Document processing can be streamlined by NLP applications reading and routing them to the appropriate department. In addition, based upon the document type, an automated look-up script could be run to verify information in the document.
Beyond these and other traditional applications, such as chatbots or interactive websites, cutting-edge data science technologies can already deliver relevant and scalable products and services to the public sector, including: social network analysis for online public opinion, email classification for targeted, responsive governance, facial recognition at border controls, among others. NLP promises to make government processes simultaneously leaner and more responsive.
Strengthening automation foundations
As a key underpinning of AI, NLP has come a long way since inception, all thanks to exploding data and the technologies used to process that data. The uses of NLP span the sectors noted here and many more, however this is just the beginning of automation. There is no doubt that NLP will continue evolving and its use cases will multiple.
To Learn More
To learn more, read this paper, this paper and visit Dell Technologies HPC & AI Innovation Lab. And if you are embarking on AI and NLP projects, visit their GitHub.
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